Accelerating minimap2 for long-read sequencing applications on modern CPUs ePrints@IISc

Kalikar, S and Jain, C and Vasimuddin, M and Misra, S

(2022)

Accelerating minimap2 for long-read sequencing applications on modern CPUs.

In: Nature Computational Science, 2
(2).

pp. 78-83.

Full text not available from this repository.

Abstract

Long-read sequencing is now routinely used at scale for genomics and transcriptomics applications. Mapping long reads or a draft genome assembly to a reference sequence is often one of the most time-consuming steps in these applications. Here we present techniques to accelerate minimap2, a widely used software for this task. We present multiple optimizations using single-instruction multiple-data parallelization, efficient cache utilization and a learned index data structure to accelerate the three main computational modules of minimap2: seeding, chaining and pairwise sequence alignment. These optimizations result in an up to 1.8-fold reduction of end-to-end mapping time of minimap2 while maintaining identical output. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

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